Benchmarking reports

from sklearn_benchmarks.report import Reporting, ReportingHpo
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	35.0	17.006379
daal4py_KNeighborsClassifier	0.0	2.0	40.781562
KNeighborsClassifier_kd_tree	0.0	2.0	52.845766
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	32.120255
KMeans_tall	0.0	0.0	23.598090
daal4py_KMeans_tall	0.0	0.0	9.046897
KMeans_short	0.0	0.0	2.868130
daal4py_KMeans_short	0.0	0.0	1.450224
LogisticRegression	0.0	0.0	20.986228
daal4py_LogisticRegression	0.0	0.0	4.508078
Ridge	0.0	0.0	11.559054
daal4py_Ridge	0.0	0.0	2.074090
HistGradientBoostingClassifier	0.0	5.0	3.028100
lightgbm	0.0	5.0	55.957548
xgboost	0.0	5.0	18.885932
catboost	0.0	5.0	3.584815
total	1.0	4.0	0.390695

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.180	0.000	4.435	0.000	1	1	NaN	NaN	0.515	0.000	0.350	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.838	0.102	0.000	0.014	1	1	0.735	0.928	1.956	0.086	7.076	0.314	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.218	0.011	0.000	0.218	1	1	1.000	1.000	0.098	0.001	2.213	0.116	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.198	0.000	4.040	0.000	1	100	NaN	NaN	0.501	0.000	0.395	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.063	0.102	0.000	0.023	1	100	0.937	0.834	1.868	0.026	12.346	0.179	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.223	0.001	0.000	0.223	1	100	1.000	1.000	0.099	0.002	2.256	0.048	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.145	0.000	5.515	0.000	-1	1	NaN	NaN	0.501	0.000	0.289	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	26.618	0.288	0.000	0.027	-1	1	0.735	0.724	1.884	0.025	14.129	0.241	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.192	0.018	0.000	0.192	-1	1	1.000	1.000	0.100	0.002	1.923	0.189	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.145	0.000	5.524	0.000	-1	100	NaN	NaN	0.506	0.000	0.286	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.714	0.000	0.000	0.036	-1	100	0.937	0.928	1.959	0.024	18.232	0.225	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.199	0.012	0.000	0.199	-1	100	1.000	1.000	0.101	0.001	1.972	0.124	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.145	0.000	5.510	0.000	-1	5	NaN	NaN	0.505	0.000	0.287	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.762	0.000	0.000	0.036	-1	5	0.843	0.724	1.908	0.019	18.743	0.183	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.202	0.017	0.000	0.202	-1	5	1.000	1.000	0.101	0.002	1.996	0.171	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.138	0.000	5.805	0.000	1	5	NaN	NaN	0.509	0.000	0.271	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.082	0.173	0.000	0.023	1	5	0.843	0.834	1.930	0.014	11.958	0.123	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.220	0.002	0.000	0.220	1	5	1.000	1.000	0.103	0.007	2.141	0.156	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.287	0.000	1	1	NaN	NaN	0.102	0.000	0.544	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.821	0.032	0.000	0.011	1	1	0.975	0.989	0.343	0.005	31.530	0.489	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.006	0.000	2.351	0.156	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.283	0.000	1	100	NaN	NaN	0.102	0.000	0.554	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.389	0.086	0.000	0.021	1	100	0.987	0.989	0.283	0.005	75.623	1.393	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.001	0.000	0.028	1	100	1.000	1.000	0.006	0.000	4.713	0.253	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.277	0.000	-1	1	NaN	NaN	0.102	0.000	0.568	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.401	0.199	0.000	0.023	-1	1	0.975	0.979	0.286	0.006	81.910	1.809	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.001	0.000	0.020	-1	1	1.000	1.000	0.006	0.000	3.282	0.244	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.275	0.000	-1	100	NaN	NaN	0.102	0.000	0.572	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.809	0.000	0.000	0.034	-1	100	0.987	0.989	0.341	0.007	99.160	2.030	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.034	0.002	0.000	0.034	-1	100	1.000	1.000	0.006	0.000	5.674	0.381	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.277	0.000	-1	5	NaN	NaN	0.102	0.000	0.563	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.899	0.000	0.000	0.034	-1	5	0.982	0.979	0.288	0.006	117.569	2.473	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.034	0.002	0.000	0.034	-1	5	1.000	1.000	0.006	0.000	5.502	0.429	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.281	0.000	1	5	NaN	NaN	0.103	0.000	0.550	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.470	0.028	0.000	0.021	1	5	0.982	0.989	0.291	0.003	73.752	0.886	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.001	0.000	0.030	1	5	1.000	1.000	0.006	0.000	4.678	0.225	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.536	0.000	0.023	0.000	1	100	NaN	NaN	0.767	0.000	4.611	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	5.199	0.057	0.000	0.005	1	100	0.981	0.962	0.127	0.004	41.018	1.273	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.008	0.001	0.000	0.008	1	100	1.000	1.000	0.000	0.000	39.552	23.991	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.516	0.000	0.023	0.000	1	5	NaN	NaN	0.778	0.000	4.519	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.554	0.015	0.000	0.002	1	5	0.980	0.962	0.127	0.001	12.224	0.175	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.001	0.000	0.002	1	5	1.000	1.000	0.000	0.000	9.810	6.157	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.571	0.000	0.022	0.000	-1	1	NaN	NaN	0.748	0.000	4.773	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.486	0.004	0.000	0.000	-1	1	0.965	0.973	0.659	0.008	0.738	0.011	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.001	0.000	2.796	1.214	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.491	0.000	0.023	0.000	-1	5	NaN	NaN	0.760	0.000	4.594	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.920	0.005	0.000	0.001	-1	5	0.980	0.973	0.228	0.005	4.033	0.088	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.000	0.000	12.845	6.952	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.383	0.000	0.024	0.000	-1	100	NaN	NaN	0.740	0.000	4.572	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.981	0.043	0.000	0.003	-1	100	0.981	0.973	0.229	0.002	13.030	0.225	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.010	0.000	0.000	0.010	-1	100	1.000	1.000	0.000	0.000	29.711	13.466	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.507	0.000	0.023	0.000	1	1	NaN	NaN	0.752	0.000	4.665	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.807	0.002	0.000	0.001	1	1	0.965	0.973	0.660	0.018	1.222	0.033	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.001	0.000	1.222	0.653	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.846	0.000	0.019	0.000	1	100	NaN	NaN	0.467	0.000	1.812	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.057	0.001	0.000	0.000	1	100	0.983	0.970	0.001	0.000	74.849	31.025	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	6.049	5.305	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.863	0.000	0.019	0.000	1	5	NaN	NaN	0.475	0.000	1.816	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.000	0.001	0.000	1	5	0.979	0.970	0.001	0.000	36.703	14.334	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.618	5.061	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.823	0.000	0.019	0.000	-1	1	NaN	NaN	0.480	0.000	1.715	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.001	0.001	0.000	-1	1	0.971	0.983	0.007	0.000	3.823	0.233	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	18.519	15.750	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.822	0.000	0.019	0.000	-1	5	NaN	NaN	0.467	0.000	1.759	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.027	0.001	0.001	0.000	-1	5	0.979	0.983	0.001	0.000	24.711	7.452	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	18.977	16.038	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.826	0.000	0.019	0.000	-1	100	NaN	NaN	0.469	0.000	1.760	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.047	0.000	0.000	0.000	-1	100	0.983	0.983	0.001	0.000	46.019	13.653	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.622	17.515	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.860	0.000	0.019	0.000	1	1	NaN	NaN	0.474	0.000	1.814	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.000	0.001	0.000	1	1	0.971	0.983	0.007	0.001	3.347	0.353	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.809	3.972	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.635	0.000	0.755	0.000	k-means++	NaN	30	NaN	0.456	0.0	1.394	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.369	0.000	k-means++	0.001	30	0.000	0.000	0.0	8.300	5.549	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.411	8.845	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.503	0.000	0.954	0.000	random	NaN	30	NaN	0.407	0.0	1.238	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.374	0.000	random	0.000	30	0.001	0.000	0.0	8.314	5.371	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.055	7.622	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.846	0.000	3.506	0.000	k-means++	NaN	30	NaN	3.304	0.0	2.072	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	14.092	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.751	2.897	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.001	0.015	0.002	k-means++	1.000	30	1.000	0.000	0.0	10.663	9.414	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.358	0.000	3.775	0.000	random	NaN	30	NaN	3.159	0.0	2.013	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	15.167	0.000	random	0.002	30	0.002	0.000	0.0	5.446	3.064	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.019	0.001	random	1.000	30	1.000	0.000	0.0	9.914	8.241	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.234	0.0	0.014	0.000	k-means++	NaN	20	NaN	0.089	0.0	2.634	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.185	0.000	k-means++	-0.001	20	0.000	0.001	0.0	2.631	0.718	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.663	7.795	See	See
3	KMeans_short	fit	10000	10000	2	20	0.079	0.0	0.040	0.000	random	NaN	20	NaN	0.032	0.0	2.496	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.179	0.000	random	-0.000	20	-0.001	0.001	0.0	2.559	0.522	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.374	6.751	See	See
6	KMeans_short	fit	10000	10000	100	20	0.630	0.0	0.254	0.000	k-means++	NaN	20	NaN	0.350	0.0	1.798	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.530	0.000	k-means++	0.269	20	0.295	0.001	0.0	2.066	0.391	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	7.555	5.057	See	See
9	KMeans_short	fit	10000	10000	100	20	0.224	0.0	0.715	0.000	random	NaN	20	NaN	0.143	0.0	1.567	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	6.348	0.000	random	0.320	20	0.307	0.001	0.0	2.064	0.383	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	6.915	4.436	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.722	0.0	[-0.10065769]	0.000	NaN	NaN	NaN	NaN	NaN	2.232	0.0	5.253	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[55.64009759]	0.000	NaN	NaN	NaN	NaN	0.551	0.000	0.0	0.755	0.438	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.23791645]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.398	0.406	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.859	0.0	[2.42170759]	0.001	NaN	NaN	NaN	NaN	NaN	0.819	0.0	1.049	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[116.3626664]	0.000	NaN	NaN	NaN	NaN	0.300	0.003	0.0	0.573	0.125	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[20.36205296]	0.000	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.151	0.113	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.203	0.0	0.394	0.0	NaN	NaN	NaN	0.195	0.0	1.040	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.013	0.0	6.144	0.0	NaN	NaN	0.1	0.021	0.0	0.606	0.016	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.082	0.0	NaN	NaN	NaN	0.000	0.0	0.669	0.707	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.425	0.0	0.562	0.0	NaN	NaN	NaN	0.259	0.0	5.490	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	5.077	0.0	NaN	NaN	1.0	0.000	0.0	0.585	0.477	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.013	0.0	NaN	NaN	NaN	0.000	0.0	0.656	0.759	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting_hpo = ReportingHpo(files=[
    "results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
    "results/benchmarking/xgboost_XGBClassifier.csv",
    "results/benchmarking/lightgbm_LGBMClassifier.csv",
    "results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()

Scatter

Permutated curves

25th percentile

50th percentile

75th percentile